Optimizing Semantic Queries with bge-small-en Embedding Model: Contextual vs. Term-Based Queries
I've been experimenting with the bge-small-en embedding model for semantic queries, and I've noticed an interesting phenomenon. When I search for a term-based query compared to a more contextual sentence query with the same term, the results tend to improve significantly.
For instance, if I search for "machine learning," the results are decent. However, when I refine the query to a more contextual sentence like "What are the latest advancements in machine learning?" or "How does machine learning impact healthcare?", the relevance and quality of results seem to improve.
I'm curious about the underlying mechanism driving this improvement. Is it due to the model's ability to capture context and semantics better in sentence-level queries compared to term-based ones? Or are there other factors at play?
I'd love to hear insights and experiences from the community regarding this observation. Have you encountered similar trends with the bge-small-en model or other embedding models? What strategies do you employ to optimize queries for better results?
Looking forward to an enriching discussion!
@Shitao
Hi, @kazmi09 , more context can help model understant the query. Query expansion is a commonly used method to improve the retrieval performance. There are some latest work about query expansion: